Along with growing rise of safety monitoring requirements, city and county governments install surveillance cameras widely on roads. However, it also means that the amount of data required to be processed becomes huge, and when a case is investigated, a huge manpower is required to inspect related information. Therefore, various police units actively seek solutions for automatic video content analysis, for example, moving object detection, object classification, license plate detection and recognition, etc., so as to accelerate a case-handling process.
Besides image analysis of a single camera, another automation function which is able to assist the police to handle the case is cross-camera object association, and an application example thereof is to lock an object to be searched at first (for example, a suspect, a suspect car or a stolen car, etc.), and then find appearance time of the same object from different cameras, so as to connect a moving track of the object on a map, which can be facilitated to determine the possible behavior of the object or the possible location that could be appeared subsequently, so as to intercept any suspicious objects as soon as possible.
In the current surveillance camera network, due to different resolutions of the cameras and different light and shadow conditions, accuracy of the automated analysis algorithm is still required to be improved. Since the quantity of the surveillance cameras is huge, the amount of captured video data is huge, and it is time-consuming in data processing. Therefore, how to obtain accurate searching results effectively may be an issue to be developed.